Elder Abuse: Selected Papers from the Prague World Congress on Family Violence
Bibliographic record
Abstract
* Foreword * Introduction * Grandparents Raising Grandchildren: The Risks of Caregiving (Patricia Brownell, Jacqueline Berman, Antoinette Nelson, and Rosemary Colon Fofana) * The Elder Abuse of Custodial Grandparents: A Hidden Phenomenon (Jordan I. Kosberg and Gordon MacNeil) * Combating Elder Financial Abuse--A Multi-Disciplinary Approach to a Growing Problem (Betty Malks, Jamie Buckmaster, and Laura Cunningham) * Study of Elder Abuse Within Diverse Cultures (Jordan I. Kosberg, Ariela Lowenstein, Juanita L. Garcia, and Simon Biggs) * A National Look at Elder Abuse Multidisciplinary Teams (Pamela B. Teaster, Lisa Nerenberg, and Kim L. Stansbury) * A Forensic Medical Examination Form for Improved Documentation and Prosecution of Elder Abuse (Diana Koin) * Elder Abuse Awareness in Faith Communities: Findings from a Canadian Pilot Study (Elizabeth Podnieks and Sue Wilson) * An Exploratory Study of Responses in Elder Abuse in Faith Communities (Elizabeth Podnieks and Sue Wilson) * Older Women, Domestic Violence, and Elder Abuse: A Review of Commonalities, Differences and Shared Approaches (Bridget Penhale) * Elder Abuse Risk Indicators and Screening Questions: Results from a Literature Search and a Panel of Experts from Developed and Developing Countries (Christen L. Erlingsson, Sharon L. Carlson, and Britt-Inger Saveman) * Index * Reference Notes Included
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".